A Day in the Life of a Data Engineer
The first time I considered becoming a data professional was in my last year of college when I took a class in machine learning and big data. I saw the potential impact and use cases of machine learning that have simply not been seen before. Even... Read more
Using NLP to identify Adverse Drug Events (ADEs)
An adverse drug event (ADE) is defined as harm experienced by a patient as a result of exposure to a medication. A significant amount of information about drug-related safety issues such as adverse effects is published in medical case reports that usually can only be explored by... Read more
Causal Reasoning in Machine Learning
Thanks to recent advancements in Artificial Intelligence (AI), we are now able to leverage Machine Learning and Deep Learning technologies in both academic and commercial applications. Although, relying just on correlations between the different features, can possibly lead to wrong conclusions since correlation does not necessarily... Read more
5 Unexpected Industries Utilizing AI and ML
Artificial intelligence (AI) and machine learning (ML) are changing the world around us in extraordinary ways, including some surprising new applications in global industries. These technologies enable us to take computing to a whole new level, which opens the door for innovations that improve processing capabilities,... Read more
Invisible Skills That Distinguish Expert Data Scientists
Although people have been doing statistics with programming for a while, we are still in the early stages of the formal function of data science and machine learning engineering in the industry. So far, we have a well-established list of hard technical skills that one can... Read more
6 Vital Parts of Data Processing
Big data is becoming more prevalent in the business landscape. The global market is expected to reach an estimated $103 billion by 2027. The world of vast information is complex and often ambiguous to the average person. Companies need to leverage data to remain competitive in... Read more
Setting up a Text Summarization Project
When OpenAI released the third generation of their machine learning (ML) model that specializes in text generation in July 2020, I knew something was different. This model struck a nerve like no one that came before it. Suddenly I heard friends and colleagues, who might be interested... Read more
What’s the Main Priority for Data Labeling in Modern ML, Quality or Scale: Experts Weigh In
AI relies on data labeling for training algorithms – without the data, there can’t be any machine learning. Data labeling accuracy is often difficult to achieve on its own, but it becomes even more of an issue when scalability is at stake. It’s believed by many... Read more
5 Tools for Getting Started with Data Science on GitHub
Depending on who you ask, the definition of “data scientist” can vary from “Excel expert” to “deep learning engineer” to “MLOps practitioner” – working individually, or as part of a team. Given this broad spectrum of software engineering experience, it can be challenging for data scientists... Read more
What Is the Difference Between Test Data and Live Data?
Data has become a hot topic of discussion, but many of these conversations oversimplify what it covers. Those outside of data science operations refer to data as a singular resource or group when there are actually many different forms of data that serve various purposes. One... Read more